Epidemiology and biology of physical activity and cancer recurrence
Bibliographic record
Abstract
Physical activity is emerging from epidemiologic research as a lifestyle factor that may improve survival from colorectal, breast, and prostate cancers. However, there is considerably less evidence relating physical activity to cancer recurrence and the biologic mechanisms underlying this association remain unclear. Cancer patients are surviving longer than ever before, and fear of cancer recurrence is an important concern. Herein, we provide an overview of the current epidemiologic evidence relating physical activity to cancer recurrence. We review the biologic mechanisms most commonly researched in the context of physical activity and cancer outcomes, and, using the example of colorectal cancer, we explore hypothesized mechanisms through which physical activity might intervene in the colorectal recurrence pathway. Our review highlights the importance of considering pre-diagnosis and post-diagnosis activity, as well as cancer stage and timing of recurrence, in epidemiologic studies. In addition, more epidemiologic research is needed with cancer recurrence as a consistently defined outcome studied separately from survival. Future mechanistic research using randomized controlled trials, specifically those demonstrating the exercise responsiveness of hypothesized mechanisms in early stages of carcinogenesis, are needed to inform recommendations about when to exercise and to anticipate additive or synergistic effects with other preventive behaviors or treatments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".